Behaviors in REM sleep: AI assisted 3D Video Analysis

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by abnormal jerks and dream-enacting behaviors in REM sleep. RBD, in its isolated form (iRBD), is the early stage of alpha-synucleinopathies (Parkinsons disease, dementia with Lewy bodies and multiple system atrophy), therefore its correct diagnosis will be fundamental when neuroprotective therapies will be available. Currently, iRBD can be diagnosed in specialized sleep centers with video-polysomnography (v-PSG), which requires time-consuming visual analyses. As the hallmarks of iRBD are minor dream-related motor events which might remain unnoticed, patients are often missed in the general population. This is particularly true for women, for whom the symptoms are generally mild. Furthermore, no objective outcomes are available to monitor symptomatic treatment for iRBD patients. Therefore, new technologies for automatic identification of iRBD patients and personalized follow-up in home environments are needed. 3D contactless video based on the time-of-flight principle is an emerging tool useful for this. However, the devices for recording 3D videos employed so far have never been validated against a wide range of RBD differential diagnoses, and are not appropriate for at- home and stand-alone recordings. In the context of the ERA PerMed Transnational Call 2021, the Sleep Disorder Clinic of the Department of Neurology of the Medical University of Innsbruck will coordinate a transnational European project aiming to validate personalized artificial intelligence algorithms that use 3D videos recorded with small, light and portable sensors as novel stand -alone technology for automatic identification and follow-up of iRBD patients. The new technology will be validated against gold-standard v-PSG in a total of five different European expert centers for sleep and neurology. By the end of the project, it is expected that 3D video will be validated and ready to be established as a novel and powerful stand-alone technique to reliably identify and follow-up iRBD patients. This novel tool can revolutionize the way in which iRBD patients are identified, allowing better identification of early-stage alpha-synucleinopathies. Furthermore, this technology will allow having objective measures of the efficacy of symptomatic treatments, thus making it possible to personalize them.